• The tentative course outline is as follows. The weekly coverage might change as it depends on the progress of the class.

• Expected hours of study are 7.5 hours per week (with lecture times included).

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Week 1 (9/15) - Course Introduction & Regression Fundamentals

Week 2 (9/22) - R Tutorials

Week 3 (9/29) - Review of Linear Regression, Least Squares and Instrumental Variables

Week 4 (10/6) - Review of Linear Regression, Least Squares and Instrumental Variables

Week 5 (10/13) - Robust and Clustered Standard Errors (Empirical research proposal due)

Week 6 (10/20) - Robust and Clustered Standard Errors

Week 7 (10/27) - Simulation and Bootstrap Methods

Week 8 (11/3) - Midterm exam

Week 9 (11/10) - Discrete Choice Analysis

Week 10 (11/17) - Discrete Choice Analysis; Basic Panel Data Models

Week 11 (11/24) - Basic Panel Data Models

Week 12 (12/1) - Difference in Differences

Week 13 (12/8) - Synthetic Controls

Week 14 (12/15) - Machine Learning for Econometrics

Week 15 (12/22) - Machine Learning for Econometrics

Week 16 (12/29) - Distributional methods (if time permits)

Week 17 (1/5) - Empirical Research Project (No Class)

Week 18 (1/12) - Empirical Research Project & Empirical research paper due (No Class)

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• Problem sets

• The problem sets will include both problem solving and computer tasks. The assignments will be reviewed in class if necessary.

• You are encouraged to form a study group with your classmates, but you must write up your own answers. Problem sets with identical answers will NOT be accepted.

• Group empirical research project (with 4-5 students per group)

• There will be a group project on empirical research, aiming to provide students with experience in applying the statistical and econometric methods examined in the course.

• The task is to take a published article of interest, replicate its numerical results, and then extend the analysis in some way. Possible extensions include different data and modifications of model specification.

• The empirical research paper will be evaluated with respect to clarity of exposition, thoroughness of description of the data and methods, competence in using the methods, and thoughtfulness in interpreting results. Complexity of economic theory and econometric methods does not carry weight in the evaluation.

TBA.

• Class participation (10%)

• Problem sets (30%)

• The midterm exam (30%)

• The empirical research proposal & paper (30%)

There is no required texts for this course. The supplementary texts listed below are optional and recommended:

• Econometrics, by Bruce Hansen, Princeton University Press, 2022.

• Causal Inference - The Mixtape, by Scott Cunningham, Yale University Press, 2021.

• Mostly Harmless Econometrics: An Empiricist's Companion, by Angrist & Pischke, Princeton University Press, 2009.

• Applied Regression Analysis & Generalized Linear Models, by John Fox, 3rd edition, SAGE Publications, Inc., 2016.

• An R Companion to Applied Regression, by Fox and Weisberg, 3rd edition, SAGE Publications, Inc., 2019.

• Applied Econometrics with R, by Kleiber and Zeileis, Springer-Verlag, 2008. (Chapters 1-5 & 7)

• Introduction to Econometrics with R, by Hanck, Arnold, Gerber, and Schmelzer, 2018. (Chapters 1-7)